You’re a data analyst in internet services. What are the most important skills you need to learn?

2 Data structures and algorithms

Data structures and algorithms are the building blocks of data analysis. They help you organize, store, and access data efficiently and effectively. You should know how to use different types of data structures, such as arrays, lists, dictionaries, sets, trees, graphs, and heaps. You should also know how to implement and apply common algorithms, such as sorting, searching, hashing, recursion, dynamic programming, and graph traversal. These skills will help you solve complex problems and optimize your code performance.

3 Statistics and mathematics

Statistics and mathematics are the foundations of data analysis. They help you understand, interpret, and communicate data. You should know how to use descriptive statistics, such as mean, median, mode, standard deviation, and quartiles, to summarize and explore data. You should also know how to use inferential statistics, such as hypothesis testing, confidence intervals, correlation, and regression, to draw conclusions and make predictions from data. Moreover, you should have a solid grasp of basic mathematics concepts, such as algebra, calculus, linear algebra, and discrete mathematics, to support your data analysis.

4 Data visualization and communication

Data visualization and communication are the skills you need to present and share your data analysis results. They help you tell a story with data, highlight key insights, and persuade your audience. You should know how to use different types of charts, graphs, maps, and dashboards to display data in a clear and engaging way. You should also know how to use tools and software that can help you create and customize data visualizations, such as Tableau, Power BI, or D3.js. Furthermore, you should know how to write and speak effectively, using appropriate language, tone, and format, to communicate your data analysis findings.

5 Data sources and tools

Data sources and tools are the resources you need to access and manage data. They help you collect, store, and retrieve data from various sources, such as databases, APIs, web scraping, or surveys. You should know how to use different types of data sources, such as relational, non-relational, structured, unstructured, or semi-structured data. You should also know how to use tools and platforms that can help you handle and process data, such as Excel, Google Sheets, Google Analytics, or AWS.

6 Data ethics and privacy

Data ethics and privacy are the principles and practices you need to follow to ensure that your data analysis is ethical and respectful of the rights and interests of the data subjects and stakeholders. They help you avoid potential legal, moral, or social issues that may arise from your data analysis. You should know how to comply with relevant laws and regulations, such as GDPR, CCPA, or HIPAA, that govern data protection and privacy. You should also know how to apply ethical frameworks and guidelines, such as FAIR, CARE, or RAP, that promote responsible and accountable data analysis.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?